Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.align = "center",
fig.show = "asis",
eval = TRUE,
tidy.opts = list(blank = FALSE, width.cutoff = 60),
tidy = TRUE,
message = FALSE,
warning = FALSE
)
## ----install-pkg, eval=FALSE--------------------------------------------------
# ## install release version of NFP
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("NFP")
## ----nstall-pkg2, eval=FALSE--------------------------------------------------
# ## install release version of NFP
# BiocManager::install(c("graph","KEGGgraph"))
# install.packages("NFP")
## ----install-pkg-github, eval=FALSE-------------------------------------------
# ## install NFP from github, require biocondutor dependencies package pre-installed
# if (!require(devtools))
# install.packages("devtools")
# devtools::install_github("yiluheihei/NFP")
## ----load-pkg,eval=TRUE, include=FALSE----------------------------------------
library(NFP)
## ----eval=FALSE---------------------------------------------------------------
# if (!require("NFPdata")) {
# install_data_package()
# }
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
## donot run, retrive pathway maps from KEGG database may take several minutes,
## we have pre-stored this data in our package
## kegg_refnet <- load_KEGG_refnet(organism = 'hsa')
data(kegg_refnet)
# show the kegg reference networks
show(kegg_refnet)
## ----keggrefnet-methods,echo=TRUE,eval=TRUE-----------------------------------
## group information of kegg reference networks
refnet_group <- group(kegg_refnet)
show(refnet_group)
## select goup 1 and 2, and subset this two groups
selected_group <- refnet_group$name[c(1,2)]
NFPnet <- subnet(kegg_refnet,selected_group)
NFPnet
## ----reactome-map-------------------------------------------------------------
## Reactome human pathway maps
require(graphite)
human_pathway <- pathways("hsapiens", "kegg")
## just choose first two pathway maps for testing
p <- human_pathway[1:2]
show(p)
g <- lapply(p, pathwayGraph)
show(g)
## ----customize-refnet,echo=TRUE, eval=TRUE------------------------------------
## here, just take the above two reactome pathway maps as NFP basic reference
## networks as example
g_names <- names(human_pathway)[1:2]
## only one group and two reference networks
customized_refnet <- new("NFPRefnet",network = list(g), name = list(g_names),
group = "test group", organism ='hsa')
## methods of NFPRefnet
show(customized_refnet)
group(customized_refnet)
subnet(customized_refnet, 'test group', 1)
## ----calc-NFP, eval=FALSE, echo=TRUE------------------------------------------
# ## set g as the query network
# query_net <- g
# ## a subset of kegg_refnet, select the head five networks of group 1, 2
# group_names <- group(kegg_refnet)$name
# sample_NFPRefnet <- subnet(kegg_refnet, group_names[1:2],list(1:5,1:5))
#
# ## In order to save calculating time, we take nperm = 10
# NFP_score <- lapply(query_net, calc_sim_score,NFPnet = sample_NFPRefnet,
# nperm = 10)
#
# ## methods of NFP class
# show(NFP_score[[1]])
# randomized_score <- perm_score(NFP_score[[1]])
# cluster <- cluster_info(NFP_score[[1]])
## ----plot-nfp,echo=TRUE,eval=FALSE,fig.cap='Plot a NFP object',dev='pdf',fig.show='hold',out.width='.8\\linewidth', out.height='.7\\linewidth'----
# plot_NFP(NFP_score[[1]])
## -----------------------------------------------------------------------------
knitr::include_graphics("nfp_plot.png")
## ----echo=FALSE---------------------------------------------------------------
sessionInfo()
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